{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-01-07T14:04:55.454494Z",
     "start_time": "2025-01-07T14:04:55.446513Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "us_file_path = \"../youtube_video_data/US_video_data_numbers.csv\"\n",
    "uk_file_path = \"../youtube_video_data/GB_video_data_numbers.csv\"\n",
    "\n",
    "# loadtxt() function is used to read data from a text file.\n",
    "#  delimiter 划分符\n",
    "t_us = np.loadtxt(us_file_path, delimiter=\",\", dtype=int)\n",
    "\n",
    "# 取最后一列\n",
    "t_us_comments = t_us[:, -1]\n",
    "print(t_us_comments.shape)  # (1688,)\n",
    "\n",
    "# 取评论数小于等于5000的视频 ，相当于清洗数据\n",
    "t_us_comments = t_us_comments[t_us_comments <= 5000]\n",
    "\n",
    "# 清洗后留下的数据\n",
    "print(t_us_comments.shape)  # (1373,)\n",
    "\n",
    "# 数据的最小值和最大值\n",
    "print(t_us_comments.max(), t_us_comments.min())  # 4995 0\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1688,)\n",
      "(1373,)\n",
      "4995 0\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-07T14:14:09.255099Z",
     "start_time": "2025-01-07T14:14:09.056345Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 画出评论数的直方图\n",
    "distance = 50  # 间隔\n",
    "bin_num = (t_us_comments.max() - t_us_comments.min()) // distance\n",
    "\n",
    "# 画直方图\n",
    "plt.figure(figsize=(20, 8), dpi=80)\n",
    "\n",
    "plt.hist(t_us_comments, bins=bin_num, color='blue', alpha=0.5, label='US')\n",
    "\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ],
   "id": "76010e7f0c08e8f0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-07T14:20:45.075432Z",
     "start_time": "2025-01-07T14:20:44.978316Z"
    }
   },
   "cell_type": "code",
   "source": [
    "t_uk = np.loadtxt(uk_file_path, delimiter=\",\", dtype=int)\n",
    "\n",
    "#取出来英国的youtube评论数和喜欢数,第1列是like数，第3列是comment数\n",
    "\n",
    "t_uk = t_uk[t_uk[:, 1] <= 250000]  # 取评论数小于等于250000的视频\n",
    "print(t_uk.shape)  # (1556, 4)\n",
    "\n",
    "t_uk_like = t_uk[:, 1]  # 取出like数列，第一列，作为横轴坐标\n",
    "t_uk_comments = t_uk[:, -1]  # 取出评论数列，最后一列，作为纵轴坐标\n",
    "\n",
    "plt.figure(figsize=(20, 8), dpi=80)\n",
    "\n",
    "# 打印横轴纵轴标签\n",
    "plt.xlabel('like')\n",
    "plt.ylabel('comments')\n",
    "plt.scatter(t_uk_like, t_uk_comments, s=10, alpha=0.5, color='red', label='UK')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ],
   "id": "a0b910f12615c681",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1556, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
